Methods are known that provide machine-controlled cropping of digital images. The term “machine-controlled” is used herein to refer to an automated process performed by machine, such as a programmed computer, without human intervention. Machine-controlled cropping can be arbitrary relative to image content, but that approach has the risk that a main subject in an image will be cropped.
U.S. Pat. No. 6,282,317, to Luo et al, discloses a method, in which a main subject in a digital image is detected. The image is segmented into regions of arbitrary shapes. The regions are grouped into larger regions based on similarity measures, and are further grouped by purposive grouping, in which the regions are evaluated for their saliency using structural saliency features and semantic saliency features. The evidences of both types of saliency features are integrated using a Bayes net reasoning engine to yield a final belief map of the main subject. In one embodiment, the semantic saliency feature detection includes use of a skin detector, followed by use of a face detector in detected skin regions.
U.S. Pat. No. 6,654,506, to Luo et al, and U.S. Patent Application Publication No. US2005/0025387 A1 use the main subject detector of U.S. Pat. No. 6,282,317 in machine-controlled cropping of images. The output of the main subject detector can be thresholded using a clustering process to provide high, medium, and low values corresponding to a main subject, secondary subject, and background. These approaches have the shortcoming that the cropping provided is sometimes excessive.
It would thus be desirable to provide improved methods, computer program products, and systems that overcome these shortcomings.